*
     * Consequently:
     * 1. If the previous mean is finite and the new value is non-finite then the new mean is that
     *    value (whether it is NaN or infinity).
     * 2. If the new value is finite and the previous mean is non-finite then the mean is unchanged
     *    (whether it is NaN or infinity).
     * 3. If both the previous mean and the new value are non-finite and...

   * is not guaranteed to return zero when the dataset consists of the same value multiple times,
   * due to numerical errors. However, it is guaranteed never to return a negative result.
   *
   * <h3>Non-finite values</h3>
   *
   * <p>If the dataset contains any non-finite values ({@link Double#POSITIVE_INFINITY}, {@link

          -20,
          Double.NaN,
          Double.NEGATIVE_INFINITY,
          -0.001);

  public void testFuzzyEqualsFinite() {
    for (double a : FINITE_DOUBLE_CANDIDATES) {
      for (double b : FINITE_DOUBLE_CANDIDATES) {
        for (double tolerance : FINITE_TOLERANCE_CANDIDATES) {
          assertEquals(Math.abs(a - b) <= tolerance, DoubleMath.fuzzyEquals(a, b, tolerance));
        }
      }
    }
  }

    assertThat(TWO_VALUES_PAIRED_STATS.populationCovariance())
        .isWithin(ALLOWED_ERROR)
        .of(TWO_VALUES_SUM_OF_PRODUCTS_OF_DELTAS / 2);
    // For datasets of many double values, we test many combinations of finite and non-finite
    // x-values:
    for (ManyValues values : ALL_MANY_VALUES) {
      PairedStats stats = createPairedStatsOf(values.asIterable(), OTHER_MANY_VALUES);
      double populationCovariance = stats.populationCovariance();

    assertThat(TWO_VALUES_PAIRED_STATS.populationCovariance())
        .isWithin(ALLOWED_ERROR)
        .of(TWO_VALUES_SUM_OF_PRODUCTS_OF_DELTAS / 2);
    // For datasets of many double values, we test many combinations of finite and non-finite
    // x-values:
    for (ManyValues values : ALL_MANY_VALUES) {
      PairedStats stats = createPairedStatsOf(values.asIterable(), OTHER_MANY_VALUES);
      double populationCovariance = stats.populationCovariance();

          -20,
          Double.NaN,
          Double.NEGATIVE_INFINITY,
          -0.001);

  public void testFuzzyEqualsFinite() {
    for (double a : FINITE_DOUBLE_CANDIDATES) {
      for (double b : FINITE_DOUBLE_CANDIDATES) {
        for (double tolerance : FINITE_TOLERANCE_CANDIDATES) {
          assertEquals(Math.abs(a - b) <= tolerance, DoubleMath.fuzzyEquals(a, b, tolerance));
        }
      }
    }
  }

    assertThat(TWO_VALUES_STATS.mean()).isWithin(ALLOWED_ERROR).of(TWO_VALUES_MEAN);
    // For datasets of many double values created from an array, we test many combinations of finite
    // and non-finite values:
    for (ManyValues values : ALL_MANY_VALUES) {
      double mean = Stats.of(values.asArray()).mean();
      if (values.hasAnyNaN()) {
        assertWithMessage("mean of " + values).that(mean).isNaN();

   *
   * <p>This is guaranteed to return zero if the dataset contains a single pair of finite values. It
   * is not guaranteed to return zero when the dataset consists of the same pair of values multiple
   * times, due to numerical errors.
   *
   * <h3>Non-finite values</h3>
   *
   * <p>If the dataset contains any non-finite values ({@link Double#POSITIVE_INFINITY}, {@link

 */

package com.google.common.math;

import static com.google.common.math.MathTesting.ALL_BIGINTEGER_CANDIDATES;
import static com.google.common.math.MathTesting.FINITE_DOUBLE_CANDIDATES;
import static com.google.common.math.MathTesting.POSITIVE_FINITE_DOUBLE_CANDIDATES;
import static org.junit.Assert.assertThrows;

import java.lang.reflect.Method;
import java.math.BigInteger;
import junit.framework.TestCase;

/**

        .isWithin(ALLOWED_ERROR)
        .of(MANY_VALUES_MEAN);
    // For datasets of many double values created from an iterable, we test many combinations of
    // finite and non-finite values:
    for (ManyValues values : ALL_MANY_VALUES) {
      StatsAccumulator accumulator = new StatsAccumulator();
      StatsAccumulator accumulatorByAddAllStats = new StatsAccumulator();